CS6501: Deep Learning for Visual Recognition Object Detection I: RCNN, Fast-RCNN, Faster-RCNN
Today s Class Object Detection The RCNN Object Detector (2014) The Fast RCNN Object Detector (2015) The Faster RCNN Object Detector (2016)
Object Detection deer cat
Object Detection as Classification CNN deer? cat? background?
Object Detection as Classification CNN deer? cat? background?
Object Detection as Classification CNN deer? cat? background?
Object Detection as Classification with Sliding Window CNN deer? cat? background?
Object Detection as Classification with Box Proposals
RCNN https://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf Rich feature hierarchies for accurate object detection and semantic segmentation. Girshick et al. CVPR 2014.
RCNN First stage: generate categoryindependent region proposals. 2000 Region proposals for every image Selective Search: combine the strength of both an exhaustive search and segmentation. Uijlings et al. IJCV 2013. ref
RCNN First stage: generate categoryindependent region proposals. 2000 Region proposals for every image Second stage: extracts a fixed-length feature vector from each region. a 4096-dimensional feature vector from each region proposal warp CNN feature vector Arbitrary rectangles? A fixed size input? 227 x 227 5 conv layers + 2 fully connected layers
RCNN First stage: generate categoryindependent region proposals. 2000 Region proposals for every image Second stage: extracts a fixed-length feature vector from each region. a 4096-dimensional feature vector from each region proposal Third stage: a set of class- specific linear SVMs. object category and location feature vector proposal location linear svm Bounding box regression people? horse? background? x y w h
RCNN Fast-RCNN Simple and scalable. improves map. A multistage pipeline. Training is expensive in space and time (features are extracted from each region proposal in each image and written into disk). Object detection is slow.?
Fast-RCNN https://arxiv.org/abs/1504.08083 Fast R-CNN. Girshick. ICCV 2015. Idea: No need to recompute features for every box independently
Fast-RCNN Process the whole image with several convolutional (conv) and max pooling layers to produce a conv feature map. a region of interest (RoI) pooling layer extracts a fixed-length feature vector from the region feature map. feature vector FC+ softmax K + 1 categories + FC+ regressor four real-valued numbers for each of the K object classes.
RCNN Simple and scalable. improves map. A multistage pipeline. Training is expensive in space and time (features are extracted from each region proposal in each image and written into disk). Object detection is slow. Fast-RCNN Higher map. Single stage, end-to-end training. No disk storage is required for feature caching. proposals are the computational bottleneck in detection systems. Faster-RCNN?
Faster-RCNN Idea: Integrate the Bounding Box Pro posals as part of the CNN predictions https://arxiv.org/abs/1506.01497 Ren et al. NIPS 2015.
Faster-RCNN Region Proposal Networks: 2k scores 4k coordinates k anchors boxes cls layer object or not object 1x1 conv layer bounding box proposal 1x1 conv layer reg layer RPN nxn conv layer Shared conv layers Fast-RCNN feature map sliding window, nxn
RCNN Simple and scalable. improves map. A multistage pipeline. Training is expensive in space and time (features are extracted from each region proposal in each image and written into disk). Object detection is slow. Fast-RCNN Higher map. Single stage, end-to-end training. No disk storage is required for feature caching. proposals are the computational bottleneck in detection systems. Faster-RCNN compute proposals with a deep convolutional neural network --Region Proposal Network (RPN) merge RPN and Fast R-CNN into a single network, enabling nearly cost-free region proposals.?
YOLO- You Only Look Once Idea: No bounding box proposal. A single regression problem, stra ight from image pixels to boundi ng box coordinates and class pro babilities. extremely fast reason globally learn generalizable represent ations https://arxiv.org/abs/1506.02640 Redmon et al. CVPR 2016.
YOLO- You Only Look Once Divide the image into 7x7 cells. Each cell trains a detector. The detector needs to predict the object s class distributions. The detector has 2 bounding-box predictors to predict bounding-boxes and confidence scores.
SSD: Single Shot Detector Idea: Similar to YOLO, but denser grid map, multiscale grid maps. + Data augme ntation + Hard negative mining + Other design choices in the network. Liu et al. ECCV 2016.
Questions?